While scenario analysis and stress testing have been an explicit part of risk management methodologies and systems for over two decades, the typical scenario and stress testing tools haven’t evolved much and are still generally quite static and largely subjective. In this talk, we discuss advanced approaches to create meaningful stress scenarios for risk management and regulatory stress testing, which effectively combine economic forecasts and “expert” views with portfolio simulation methods. Regulatory and expert scenarios are typically described in terms of a small number of key economic variables or factors. However, when applied to a portfolio, they are incomplete – they generally do not describe what occurs to all relevant market risk and credit risk factors that affect a portfolio. We need to understand how these risk factors behave, conditional on the outcome of the economic factors, and the map this to portfolio losses. In particular we introduce a new approach called Least Squares Stress Testing (LSST). The key insight is that the conditional expectation, and more generally the full conditional distribution of all the factors, and of the portfolio P&L, can be estimated directly from a pre-computed simulation using Least Squares Regression. LSST is a simulation-based conditional scenario generation method that offers many advantages over more traditional analytical methods. Simulation techniques are simple, flexible, and provide very transparent results, which are auditable and easy to explain. LSST can be applied to both market and credit risk stress testing with a large number of risk factors, which can follow completely general stochastic processes, with fat-tails, non-parametric and general codependence structures, autocorrelation, etc. LSST further produces explicit risk factor P&L contributions. From a methodology perspective, we also discuss some of the assumptions the LSST approach, statistical tests to check when these assumptions fail, and remedies that can be applied. We illustrate the application of the methodology through real-life applications to market risk and credit risk portfolios.